Papers Containing Keywords(s): 'residence'
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Viewing papers 1 through 10 of 63
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Working PaperThe Effects of Eviction on Children
May 2025
Working Paper Number:
CES-25-34
Eviction may be an important channel for the intergenerational transmission of poverty, and concerns about its effects on children are often raised as a rationale for tenant protection policies. We study how eviction impacts children's home environment, school engagement, educational achievement, and high school completion by assembling new data sets linking eviction court records in Chicago and New York to administrative public school records and restricted Census records. To disentangle the consequences of eviction from the effects of correlated sources of economic distress, we use a research design based on the random assignment of court cases to judges who vary in their leniency. We find that eviction increases children's residential mobility, homelessness, and likelihood of doubling up with grandparents or other adults. Eviction also disrupts school engagement, causing increased absences and school changes. While we find little impact on elementary and middle school test scores, eviction substantially reduces high school course credits. Lastly, we find that eviction reduces high school graduation and use a novel bounding method to show that this finding is not driven by differential attrition. The disruptive effects of eviction appear worse for older children and boys. Our evidence suggests that the impact of eviction on children runs through the disruption to the home environment or school engagement rather than deterioration in school or neighborhood quality, and may be moderated by access to family support networks.View Full Paper PDF
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Working PaperGeographic Immobility in the United States: Assessing the Prevalence and Characteristics of Those Who Never Migrate Across State Lines Using Linked Federal Tax Microdata
March 2025
Working Paper Number:
CES-25-19
This paper explores the prevalence and characteristics of those who never migrate at the state scale in the U.S. Studying people who never migrate requires regular and frequent observation of their residential location for a lifetime, or at least for many years. A novel U.S. population-sized longitudinal dataset that links individual level Internal Revenue Service (IRS) and Social Security Administration (SSA) administrative records supplies this information annually, along with information on income and socio-demographic characteristics. We use these administrative microdata to follow a cohort aged between 15 and 50 in 2001 from 2001 to 2016, differentiating those who lived in the same state every year during this period (i.e., never made an interstate move) from those who lived in more than one state (i.e., made at least one interstate move). We find those who never made an interstate move comprised 75 percent of the total population of this age cohort. This percentage varies by year of age but never falls below 62 percent even for those who were teenagers or young adults in 2001. There are also variations in these percentages by sex, race, nativity, and income, with the latter having the largest effects. We also find substantial variation in these percentages across states. Our findings suggest a need for more research on geographically immobile populations in U.S.View Full Paper PDF
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Working PaperGeographic Disparities in Alzheimer's Disease and Related Dementia Mortality in the US: Comparing Impacts of Place of Birth and Place of Residence
January 2025
Working Paper Number:
CES-25-11
Objective: Building on the hypothesis that early-life exposures might influence the onset of Alzheimer's Disease and Related Dementia (ADRD), this study delves into geographic variations in ADRD mortality in the US. By considering both state of residence and state of birth, we aim to discern the comparative significance of these geospatial factors. Methods: We conducted a secondary data analysis of the National Longitudinal Mortality Study (NLMS), that has 3.5 million records from 1973-2011 and over 0.5 million deaths. We focused on individuals born in or before 1930, tracked in NLMS cohorts from 1979-2000. Employing multi-level logistic regression, with individuals nested within states of residence and/or states of birth, we assessed the role of geographical factors in ADRD mortality variation. Results: We found that both state of birth and state of residence account for a modest portion of ADRD mortality variation. Specifically, state of residence explains 1.19% of the total variation in ADRD mortality, whereas state of birth explains only 0.6%. When combined, both state of residence and state of birth account for only 1.05% of the variation, suggesting state of residence could matter more in ADRD mortality outcomes. Conclusion: Findings of this study suggest that state of residence explains more variation in ADRD mortality than state of birth. These results indicate that factors in later life may present more impactful intervention points for curbing ADRD mortality. While early-life environmental exposures remain relevant, their role as primary determinants of ADRD in later life appears to be less pronounced in this study.View Full Paper PDF
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Working PaperFood Security Status Across the Rural-Urban Continuum Before and During the COVID-19 Pandemic
January 2025
Working Paper Number:
CES-25-01
Background: Food security, defined as consistent access to sufficient food to support an active life, is a crucial social determinant of health. A key dimension affecting food security is position along the rural-urban continuum, as there are important socio-economic and environmental differences between communities related to urbanicity or rurality that impact food access. The COVID-19 pandemic created social and economic shocks that altered financial and food security, which may have had differential effects by rurality and urbanicity. However, there has been limited research on how food security differs across the shades of the rural-urban community spectrum, as most often researchers have characterized communities as either urban or rural. Methods: In this study, which linked restricted use Current Population Survey Food Security Supplement data to census-tract level United States Department of Agriculture Rural-Urban Commuting Area codes, we estimated the prevalence of household food security across temporal (2015-2019 versus 2020-2021) and socio-spatial (urban, large rural city/town, small rural town, or isolated rural town/area) dimensions in order to characterize variations before and during the COVID-19 pandemic by urbanicity/rurality. We report prevalences as point estimates with 95% confidence intervals. Results: The prevalence of food security was 87.7% (87.5-88.0%) in 2015-2019 and 88.8% (88.4-89.3%) in 2020-2021 for urban areas, 85.5% (84.7-86.2%) in 2015-2019 and 87.1% (85.7-88.3%) in 2020-2021 for large rural towns/cities, 82.8% (81.5-84.1%) in 2015-2019 and 87.3% (85.7-89.2%) in 2020-2021 for small rural towns, and 87.6% (86.3-88.8%) in 2015-2019 and 90.9% (88.7-92.7%) in 2020-2021 for isolated rural towns/areas. Conclusion: These findings show that rural communities experiences of food security vary and aggregating households in these environments may mask areas of concern and concentrated need.View Full Paper PDF
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Working PaperThe Census Historical Environmental Impacts Frame
October 2024
Working Paper Number:
CES-24-66
The Census Bureau's Environmental Impacts Frame (EIF) is a microdata infrastructure that combines individual-level information on residence, demographics, and economic characteristics with environmental amenities and hazards from 1999 through the present day. To better understand the long-run consequences and intergenerational effects of exposure to a changing environment, we expand the EIF by extending it backward to 1940. The Historical Environmental Impacts Frame (HEIF) combines the Census Bureau's historical administrative data, publicly available 1940 address information from the 1940 Decennial Census, and historical environmental data. This paper discusses the creation of the HEIF as well as the unique challenges that arise with using the Census Bureau's historical administrative data.View Full Paper PDF
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Working PaperHousehold Wealth and Entrepreneurial Career Choices: Evidence from Climate Disasters
July 2024
Working Paper Number:
CES-24-39
This study investigates how household wealth affects the human capital of startups, based on U.S. Census individual-level employment data, deed records, and geographic information system (GIS) data. Using floods as a wealth shock, a regression discontinuity analysis shows inundated residents are 7% less likely to work in startups relative to their neighbors outside the flood boundary, within a 0.1-mile-wide band. The effect is more pronounced for homeowners, consistent with the wealth effect. The career distortion leads to a significant long-run income loss, highlighting the importance of self-insurance for human capital allocation.View Full Paper PDF
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Working PaperWhose Neighborhood Now? Gentrification and Community Life in Low-Income Urban Neighborhoods
June 2024
Working Paper Number:
CES-24-29
Gentrification is a process of urban change that has wide-ranging social and political impacts, but previous studies provide divergent findings. Does gentrification leave residents feeling alienated, or does it bolster neighborhood social satisfaction? Politically, does urban change mobilize residents, or leave them disengaged? I assess a national, cross-sectional sample of about 17,500 respondents in lower-income urban neighborhoods, and use a structural equation modeling approach to model six latent variables pertaining to local social environment and political participation. Amongst the full sample, gentrification has a positive association with all six factors. However, this relationship depends upon respondents' level of income, length of residency, and racial identity. White residents and those with shorter length of residency report higher levels of social cohesion as gentrification increases, but there is no such association amongst racial minority groups and longer-term residents. This finding aligns with a perspective on gentrification as a racialized process, and demonstrates that gentrification-related amenities primarily serve the interests of white residents and newcomers. All groups, however, are more likely to participate in neighborhood politics as gentrification increases, drawing attention to the agency of local residents as they attempt to influence processes of urban change.View Full Paper PDF
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Working PaperMobility, Opportunity, and Volatility Statistics (MOVS): Infrastructure Files and Public Use Data
April 2024
Working Paper Number:
CES-24-23
Federal statistical agencies and policymakers have identified a need for integrated systems of household and personal income statistics. This interest marks a recognition that aggregated measures of income, such as GDP or average income growth, tell an incomplete story that may conceal large gaps in well-being between different types of individuals and families. Until recently, longitudinal income data that are rich enough to calculate detailed income statistics and include demographic characteristics, such as race and ethnicity, have not been available. The Mobility, Opportunity, and Volatility Statistics project (MOVS) fills this gap in comprehensive income statistics. Using linked demographic and tax records on the population of U.S. working-age adults, the MOVS project defines households and calculates household income, applying an equivalence scale to create a personal income concept, and then traces the progress of individuals' incomes over time. We then output a set of intermediate statistics by race-ethnicity group, sex, year, base-year state of residence, and base-year income decile. We select the intermediate statistics most useful in developing more complex intragenerational income mobility measures, such as transition matrices, income growth curves, and variance-based volatility statistics. We provide these intermediate statistics as part of a publicly released data tool with downloadable flat files and accompanying documentation. This paper describes the data build process and the output files, including a brief analysis highlighting the structure and content of our main statistics.View Full Paper PDF
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Working PaperWhere to Build Affordable Housing? Evaluating the Tradeoffs of Location
December 2023
Working Paper Number:
CES-23-62R
How does the location of affordable housing affect tenant welfare, the distribution of assistance, and broader societal objectives such as racial integration? Using administrative data on tenants of units funded by the Low-Income Housing Tax Credit (LIHTC), we first show that characteristics such as race and proxies for need vary widely across neighborhoods. Despite fixed eligibility requirements, LIHTC developments in more opportunity-rich neighborhoods house tenants who are higher income, more educated, and far less likely to be Black. To quantify the welfare implications, we build a residential choice model in which households choose from both market-rate and affordable housing options, where the latter must be rationed. While building affordable housing in higher-opportunity neighborhoods costs more, it also increases household welfare and reduces city-wide segregation. The gains in household welfare, however, accrue to more moderate-need, non-Black/Hispanic households at the expense of other households. This change in the distribution of assistance is primarily due to a 'crowding out' effect: households that only apply for assistance in higher-opportunity neighborhoods crowd out those willing to apply regardless of location. Finally, other policy levers'such as lowering the income limits used for means-testing'have only limited effects relative to the choice of location.View Full Paper PDF
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Working PaperProducing U.S. Population Statistics Using Multiple Administrative Sources
November 2023
Working Paper Number:
CES-23-58
We identify several challenges encountered when constructing U.S. administrative record-based (AR-based) population estimates for 2020. Though the AR estimates are higher than the 2020 Census at the national level, they are over 15 percent lower in 5 percent of counties, suggesting that locational accuracy can be improved. Other challenges include how to achieve comprehensive coverage, maintain consistent coverage across time, filter out nonresidents and people not alive on the reference date, uncover missing links across person and address records, and predict demographic characteristics when multiple ones are reported or when they are missing. We discuss several ways of addressing these issues, e.g., building in redundancy with more sources, linking children to their parents' addresses, and conducting additional record linkage for people without Social Security Numbers and for addresses not initially linked to the Census Bureau's Master Address File. We discuss modeling to predict lower levels of geography for people lacking those geocodes, the probability that a person is a U.S. resident on the reference date, the probability that an address is the person's residence on the reference date, and the probability a person is in each demographic characteristic category. Regression results illustrate how many of these challenges and solutions affect the AR county population estimates.View Full Paper PDF
